Sub-5 nm GAA-FETs based on MX (M = In, Ga; X = S, Se, Te) nanowires demonstrate excellent electrostatic control and suppressed short-channel effects, achieving outstanding performance. This promises a solution for beyond-Si CMOS technologies.

A micro-cavity based on phase-change material is a very important strategy for the realization of tunable absorption and conversion of terahertz waves. In this work, a tunable terahertz metamaterial absorber based on the phase-change material germanium–antimony–tellurium (GST) is demonstrated. The device features a metal–insulator–metal triple-layer structure, where the dynamic switching of absorption characteristics is achieved via thermally controlled GST phase transition. In the amorphous state, the absorber exhibits a single absorption peak at 7.7 THz. Upon crystallization, the absorption switches to dual peaks at 5.1 THz and 8.3 THz, achieving near-perfect absorption in both states. Full-wave electromagnetic simulations and theoretical analysis based on a multiple-reflection interference model indicate that this performance tuning originates from the GST-phase-transition-induced change in the equivalent optical cavity length. This corresponds to a switch between two resonant modes: coupled inner–outer ring resonance and independent outer ring resonance. These results provide a foundation for developing dynamically tunable terahertz devices with promising applications in terahertz communications, imaging, and sensing.

Accurate prediction of the stress–strain fields in fiber-reinforced composites is crucial for performance analysis and structural design. However, due to their complex microstructures, traditional finite element analysis (FEA) entails a very high computational cost. Therefore, this study proposes a conditional generative adversarial network (cGAN) framework, named FiberGAN, to enable rapid prediction of the microscopic stress–strain fields in fiber-reinforced composites. The method employs an adaptive representative volume element (RVE) generation algorithm to construct random fiber arrangements with fiber volume fractions ranging from 30% to 50% and uses FEA to obtain the corresponding stress and strain fields as training data. A U-Net generator, combined with a PatchGAN discriminator, captures both global distribution patterns and fine local details. Under tensile and shear loading conditions, the R² values of FiberGAN predictions range from 0.96 to 0.99, while the structural similarity index (SSIM) values range from 0.95 to 0.99. The error maps show that prediction residuals are mainly concentrated in high-gradient regions with small magnitudes. These results demonstrate that the proposed deep learning model can successfully predict stress–strain field distributions for different fiber volume fractions under various loading conditions.